Style Match — Does This Go With That?

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, and the description aligns with this by describing a non-destructive analysis. It adds value by detailing the output: verdict, recommendations, archive color names, and historical context, which goes beyond the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: begins with a relatable question, explains input/output, and provides concrete examples. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the presence of an output schema (not shown but indicated), the description fully covers the tool's purpose, input requirements, output nature, and usage examples. It is complete for an agent to understand and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with detailed parameter descriptions. The description reinforces the input format with examples but doesn't add new semantic information beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: evaluating color compatibility of outfit items. It uses specific verbs ('submit', 'receive a verdict') and distinguishes itself from sibling tools by focusing on outfit coordination with historical color context, not generic color analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear use cases and examples ('I have a navy dress... what shoes?'), effectively guiding when to use the tool. It doesn't explicitly state when not to use it or list alternatives, but the context is strong enough for an AI agent to infer appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.